1. Field of the Invention
This invention relates to digital video processing. In particular this invention relates to using digital filters to efficiently compress video data that originates from motion picture or film.
2. Discussion of the Related Art
Under many compression standards, e.g. MPEG, the presence of noise in the video data severely degrades compression efficiency. In MPEG, for example, noise degrades interframe compression by adversely impacting the performance of "motion estimation," which achieves interframe compression by cross-referencing matching blocks of pixels in neighboring frames. In motion estimation, noise interferes with the identification of such matching blocks. In addition, noise also affects intraframe compression by reducing the correlation among neighboring pixel values, thereby reducing the compression efficiency achieved by quantizing video data transformed under a discrete cosine transform (DCT). Under fixed quantization, i.e., a variable output bit-rate approach, the noise degradation of interframe and intraframe compressions often leads to an increased output or encoded bit-rate.sup.1 by as much as 100%. The increased encoded bit-rate results in higher transmission and storage costs. Alternatively, in a storage medium of a fixed capacity, e.g. in a compact disk (CD-ROM) or a digital video disk (DVD), such increased encoded bit-rate results in content reduction. FNT .sup.1 "Bit rate" in this context refers to the number of encoded bits per unit time for a given bit-rate of uncompressed input video data.
It should be observed, however, that the impact of noise on video data is affected by two competing mechanisms. At high quantization, the high frequency noise components of the video data are quantized away to promote a lower encoded bit-rate. At low quantization, however, the noise is actually encoded in the compressed data to result in a higher encoded bit rate.
Film grain noise is a common source of noise in video data originated from a photographic film source, such as a motion picture. Film grain noise relates to the variability of photosensitive chemicals on the photographic film. Film grain noise, often causing "blotchiness" in the resulting decompressed video data, is part of the "film look" that most people desire to maintain in the encoded video. Thus, the complete removal of film grain noise is not always desirable. A detailed discussion of film grain noise can be found, for example, in an article "Estimation of Images Degraded by Film-grain Noise" by E. Naderi et al. Applied Optics, vol. 17, 1978. Naderi et al. propose the following model for film grain noise: EQU y.sub.i =X.sub.i +kn.sub.i X.sub.i.sup.1/3 (1)
where
y.sub.i is the observed pixel, x.sub.i is the original (unknown) pixel value, n.sub.i is a random noise variable ("white noise") having a Gaussian or normal distribution N(0,.sigma..sup.2), and k is an arbitrary constant.
Naderi et al. propose a filter for estimating and removing film grain noise according to this model. The non-linear term in equation (1), however, would require calculating a covariance matrix, which is too computationally intensive for a real-time application. Further, Naderi et al.'s model is applicable only to still images, i.e. noise reduction which exploits the image's spatial correlation. Since film grain noise is less correlated temporally than spatially, Naderi et al.'s model fails to exploit temporal noise reduction which is expected to yield even higher noise reduction performance.
Various locally adaptive noise reduction filtering techniques to reduce noise in video data are reported in (i) "Adaptive Noise Smoothing Filter For Images with Signal Dependent Noise" by D. Kuan et al., IEEE Transactions on Patten Analysis and Machine Intelligence, Vol. PAMI-7, No. 2, March 1985, pp. 165-177; (ii) "Digital Image Enhancement and Noise Filtering by Use of Local Statistics" by J. S. Lee, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 2, March 1989, pp. 165-168; and (iii) "Refined Filtering of Image Noise Using Local Statistics", Computer Graphics & Image Processing, Vol. 15, No. 4, April 1981, pp. 380-389. In addition, the text "Two-Dimensional Signal & Image Processing" by J. S. Lim, published by Prentice Hall, 1990, is a general text applicable to various aspects and techniques of image processing.